| import scipy |
| from scipy import signal |
| import os |
| import lmdb |
| import pickle |
| import numpy as np |
| import mne |
|
|
| tasks = ['04', '06', '08', '10', '12', '14'] |
|
|
| root_dir = '/data/datasets/eeg-motor-movementimagery-dataset-1.0.0/files' |
| files = [file for file in os.listdir(root_dir)] |
| files = sorted(files) |
|
|
| files_dict = { |
| 'train': files[:70], |
| 'val': files[70:89], |
| 'test': files[89:109], |
| } |
|
|
| print(files_dict) |
|
|
| dataset = { |
| 'train': list(), |
| 'val': list(), |
| 'test': list(), |
| } |
|
|
|
|
|
|
| selected_channels = ['Fc5.', 'Fc3.', 'Fc1.', 'Fcz.', 'Fc2.', 'Fc4.', 'Fc6.', 'C5..', 'C3..', 'C1..', 'Cz..', 'C2..', |
| 'C4..', 'C6..', 'Cp5.', 'Cp3.', 'Cp1.', 'Cpz.', 'Cp2.', 'Cp4.', 'Cp6.', 'Fp1.', 'Fpz.', 'Fp2.', |
| 'Af7.', 'Af3.', 'Afz.', 'Af4.', 'Af8.', 'F7..', 'F5..', 'F3..', 'F1..', 'Fz..', 'F2..', 'F4..', |
| 'F6..', 'F8..', 'Ft7.', 'Ft8.', 'T7..', 'T8..', 'T9..', 'T10.', 'Tp7.', 'Tp8.', 'P7..', 'P5..', |
| 'P3..', 'P1..', 'Pz..', 'P2..', 'P4..', 'P6..', 'P8..', 'Po7.', 'Po3.', 'Poz.', 'Po4.', 'Po8.', |
| 'O1..', 'Oz..', 'O2..', 'Iz..'] |
|
|
| db = lmdb.open('/data/datasets/eeg-motor-movementimagery-dataset-1.0.0/processed_average', map_size=4614542346) |
|
|
| for files_key in files_dict.keys(): |
| for file in files_dict[files_key]: |
| for task in tasks: |
| raw = mne.io.read_raw_edf(os.path.join(root_dir, file, f'{file}R{task}.edf'), preload=True) |
| raw.pick_channels(selected_channels, ordered=True) |
| if len(raw.info['bads']) > 0: |
| print('interpolate_bads') |
| raw.interpolate_bads() |
| raw.set_eeg_reference(ref_channels='average') |
| raw.filter(l_freq=0.3, h_freq=None) |
| raw.notch_filter((60)) |
| raw.resample(200) |
| events_from_annot, event_dict = mne.events_from_annotations(raw) |
| epochs = mne.Epochs(raw, |
| events_from_annot, |
| event_dict, |
| tmin=0, |
| tmax=4. - 1.0 / raw.info['sfreq'], |
| baseline=None, |
| preload=True) |
| data = epochs.get_data(units='uV') |
| events = epochs.events[:, 2] |
| print(data.shape, events) |
| data = data[:, :, -800:] |
| bz, ch_nums, _ = data.shape |
| data = data.reshape(bz, ch_nums, 4, 200) |
| print(data.shape) |
| for i, (sample, event) in enumerate(zip(data, events)): |
| if event != 1: |
| sample_key = f'{file}R{task}-{i}' |
| data_dict = { |
| 'sample': sample, 'label': event - 2 if task in ['04', '08', '12'] else event |
| } |
| txn = db.begin(write=True) |
| txn.put(key=sample_key.encode(), value=pickle.dumps(data_dict)) |
| txn.commit() |
| dataset[files_key].append(sample_key) |
|
|
| txn = db.begin(write=True) |
| txn.put(key='__keys__'.encode(), value=pickle.dumps(dataset)) |
| txn.commit() |
| db.close() |
|
|
